Antibacterial Drugs and the Risk of Community-Associated Methicillin-Resistant <emph type="ital">Staphylococcus aureus</emph> in Children
Bibliographic record
Abstract
OBJECTIVE: To investigate in children the association between antibacterial drugs and subsequent diagnosis of methicillin-resistant Staphylococcus aureus (MRSA) in the community. DESIGN: Population-based case-control study in children 1 to 19 years of age. SETTING: Primary care, General Practice Research Database, United Kingdom, 1994-2007. PARTICIPANTS: Cases were children who had MRSA diagnosed as outpatients, and controls were individually matched on age and practice, with the matched case's diagnosis date as the index date for both. Main Exposures Antibacterial agents prescribed 180 to 30 days prior to the index date, excluding prescriptions 30 days before the index date to prevent protopathic bias. OUTCOME MEASURES: Rate ratios (RRs) estimated from the odds ratios of exposure in cases compared with controls using conditional logistic regression, adjusted for comorbid conditions, other prescription drug use, and hospitalization. RESULTS: The rate of MRSA was 4.5 per 100,000 per year. Of 297 cases and 9357 controls, 52.5% and 13.6%, respectively, received antibacterial drug prescriptions during the 150-day exposure window. The adjusted RR with any antibacterial drug was 3.5 (95% confidence interval [CI], 2.6-4.8). The RRs increased with the number of prescriptions (2.2 [95% CI, 1.5-3.2], 3.3 [95% CI, 1.9-5.6], 11.0 [95% CI, 5.6-21.6], and 18.2 [95% CI, 9.4-35.4] for 1, 2, 3, and ≥4 prescriptions, respectively). The RR was particularly elevated for quinolones at 14.8 (95% CI, 3.9-55.8), with wide variation among antibacterial classes. CONCLUSION: While close to half of children were diagnosed as having MRSA in the community without prior antibacterial drugs, such agents are associated with a dose-dependent increased risk, concordant with findings in adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".